Biological Significance of 18F-FDG PET/CT Maximum Standard Uptake Value for Predicting EGFR Mutation Status in Non-Small Cell Lung Cancer Patients

Association between cigarette smoking history, metabolic phenotypes, and EGFR mutation status in patients with non-small cell lung cancer

Background

Cigarette smoking exerts a significant impact on metabolic phenotypes and epidermal growth factor receptor (EGFR) mutation status; however, their correlation remains insufficiently established. Therefore, the aim of this study was to investigate the association between cigarette smoking history, metabolic phenotypes, and EGFR mutation status in patients with non-small cell lung cancer (NSCLC).

Methods

We retrospectively analyzed 198 consecutive patients with NSCLC who underwent 18F-fluoro-2-deoxy-D-glucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) before treatment and were tested for EGFR mutation status between September 2019 and March 2022. Metabolic phenotypes, including the maximum standardized uptake value (SUVmax) of the primary tumors (pSUVmax), metastatic lymph nodes (nSUVmax), and distant metastases (mSUVmax) were assessed. Patients were classified into never-smokers and smokers based on detailed smoking history. The correlations between smoking status, metabolic parameters, and EGFR mutation status were evaluated in patients with NSCLC.

Results

We observed EGFR mutations in 73 (60.3%) of 121 never-smokers and 18 (23.4%) of 77 smokers (P<0.001). EGFR-mutant NSCLC had a lower pSUVmax than that of EGFR wild-type (WT; 8.9±4.5 vs. 12.7±6.9, P<0.001). Smokers had a higher pSUVmax than never-smokers (12.5±6.4 vs. 9.9±5.9, P=0.004). With the increase of cumulative smoking dose, the pSUVmax increased significantly (r=0.198, P=0.005). There was no significant difference between nSUVmax and mSUVmax in patients with or without EGFR mutation and smoking history. Cumulative smoking dose, pSUVmax, and their combination predicted EGFR mutation status with areas under the receiver operating characteristic (ROC) curves (AUCs) 0.688, 0.673, and 0.753, respectively.

Conclusions

Our findings indicate that cigarette smoking may be one of the triggers for increased pSUVmax and decreased EGFR mutations, further suggesting that EGFR mutations are associated with low pSUVmax, which may guide clinicians in risk stratification and treatment strategy selection for patients with NSCLC.

Identification of EGFR mutation status in male patients with non-small-cell lung cancer: role of 18F-FDG PET/CT and serum tumor markers CYFRA21-1 and SCC-Ag

BACKGROUND

The high incidence of epidermal growth factor receptor (EGFR) mutations is usually found in female patients with lung adenocarcinoma who have never-smoked. However, reports concerning male patients are scarce. Thus, this study aimed to explore a novel approach based on ¹⁸F-fluoro-2-deoxy-2-deoxyglucose (¹⁸F-FDG) PET/CT and serum tumor markers (STMs) to determine EGFR mutation status in male patients with non-small-cell lung cancer (NSCLC).

METHODS

A total of 121 male patients with NSCLC were analyzed between October 2019 and March 2022. All patients underwent ¹⁸F-FDG PET/CT scan before treatment and monitored 8 STMs (cytokeratin 19 fragment [CYFRA21-1], squamous cell carcinoma-related antigen [SCC-Ag], carcinoembryonic antigen [CEA], neuron-specific enolase [NSE], carbohydrate antigen [CA] 50, CA125, CA72-4, and ferritin). A comparison was done between EGFR mutant and wild-type patients in terms of the maximum standardized uptake value of primary tumors (pSUV_max) and 8 STMs. We performed receiver operating characteristic (ROC) curve and multiple logistic regression analyses to determine predictors for EGFR mutation status.

RESULTS

EGFR mutations were detected in 39 patients (32.2%). Compared with patients with EGFR wild-type, EGFR-mutant patients had lower concentrations of serum CYRFA21-1 (2.65 vs. 4.01, P = 0.002) and SCC-Ag (0.67 vs. 1.05, P = 0.006). No significant differences of CEA, NSE, CA 50, CA125, CA72-4 and ferritin were found between the two groups. The presence of EGFR mutations was significantly associated with low pSUV_max (< 8.75), low serum SCC-Ag (< 0.79 ng/mL) and CYFRA21-1 (< 2.91 ng/mL) concentrations. The area under ROC curve values were 0.679, 0.655, 0.685 and 0.754, respectively, for low CYFRA21-1, SCC-Ag, pSUV_max and the combination of these three factors.

CONCLUSIONS

We demonstrated that low concentrations of CYFRA21-1 and SCC-Ag, as well as low pSUV_max, were associated with EGFR mutations, and that the combination of these factors resulted in a higher differentiation of EGFR mutation status in male patients with NSCLC.

The combined role of PET/CT metabolic parameters and inflammatory markers in detecting extensive disease in small cell lung cancer

The combined role of inflammatory markers [including neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), monocyte/lymphocyte ratio (MLR), and systemic immune-inflammation index (SII)] and PET/CT metabolic parameters [including maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), metabolic tumor volume (MTV), and TLG (total lesion glycolysis)] at baseline in evaluating the binary stage [extensive-stage disease (ED) and limited-stage disease (LD)] of small cell lung cancer (SCLC) is unclear. In this study, we verified that high metabolic parameters and inflammatory markers were related to the binary stage of SCLC patients, respectively (p &lt; 0.05). High inflammatory markers were also associated with high MTV and TLG in patients with SCLC (p &lt; 0.005). Moreover, the incidences of co-high metabolic parameters and inflammatory markers were higher in ED-SCLC (p &lt; 0.05) than those in LD-SCLC. Univariate logistic regression analysis demonstrated that Co-high MTV/NLR, Co-high MTV/MLR, Co-high MTV/SII, Co-high TLG/NLR, Co-high TLG/MLR, and Co-high TLG/SII were significantly related to the binary stage of SCLC patients (p = 0.00). However, only Co-high MTV/MLR was identified as an independent predictor for ED-SCLC (odds ratio: 8.67, 95% confidence interval CI: 3.51–21.42, p = 0.000). Our results suggest that co-high metabolic parameters and inflammatory markers could be of help for predicting ED-SCLC at baseline. Together, these preliminary findings may provide new ideas for more accurate staging of SCLC.

PET/CT Radiomic Features: A Potential Biomarker for EGFR Mutation Status and Survival Outcome Prediction in NSCLC Patients Treated With TKIs

Backgrounds

Epidermal growth factor receptor (EGFR) mutation profiles play a vital role in treatment strategy decisions for non–small cell lung cancer (NSCLC). The purpose of this study was to evaluate the predictive efficacy of baseline ¹⁸F-FDG PET/CT-based radiomics analysis for EGFR mutation status, mutation site, and the survival benefit of targeted therapy.

Methods

A sum of 313 NSCLC patients with pre-treatment ¹⁸F-FDG PET/CT scans and genetic mutations detection were retrospectively studied. Clinical and PET metabolic parameters were incorporated into independent predictors of determining mutation status and mutation site. The dataset was randomly allocated into the training and the validation sets in a 7:3 ratio. Three-dimensional (3D) radiomics features were extracted from each PET- and CT-volume of interests (VOI) singularly, and then a radiomics signature (RS) associated with EGFR mutation profiles is built by feature selection. Three different prediction models based on support vector machine (SVM), decision tree (DT), and random forest (RF) classifiers were established. Furthermore, nomograms for estimation of overall survival (OS) and progression-free survival (PFS) were established by integrating PET/CT radiomics score (Rad-score), metabolic parameters, and clinical factors. Predictive performance was assessed by the receiver operating characteristic (ROC) analysis and the calibration curve analysis. The decision curve analysis (DCA) was applied to estimate and compare the clinical usefulness of nomograms.

Results

Three hundred thirteen NSCLC patients were classified into a training set (n=218) and a validation set (n=95). Multivariate analysis demonstrated that SUVmax and sex were independent indicators of EGFR mutation status and mutation site. Eight CT-derived RS, six PET-derived RS, and two clinical factors were retained to develop integrated models, which exhibited excellent ability to distinguish between EGFR wild type (EGFR-WT), EGFR 19 mutation type (EGFR-19-MT), and EGFR 21 mutation type (EGFR-21-MT). The SVM model outperformed the RF model and the DT model, yielding training area under the curves (AUC) of EGFR-WT, EGFR-19-WT, and EGFR-21-WT, with 0.881, 0.851, and 0.849, respectively, and validation AUCs of 0.926, 0.805 and 0.859, respectively. For prediction of OS, the integrated nomogram is superior to the clinical nomogram and the radiomics nomogram, with C-indexes of 0.80 in the training set and 0.83 in the validation set, respectively.

Conclusions

The PET/CT-based radiomics analysis might provide a novel approach to predict EGFR mutation status and mutation site in NSCLC patients and could serve as useful predictors for the patients’ survival outcome of targeted therapy in clinical practice.

A Review of the Correlation Between Epidermal Growth Factor Receptor Mutation Status and 18F-FDG Metabolic Activity in Non-Small Cell Lung Cancer

PET/CT with 18F-2-fluoro-2-deoxyglucose (18F-FDG) has been proposed as a promising modality for diagnosing and monitoring treatment response and evaluating prognosis for patients with non-small cell lung cancer (NSCLC). The status of epidermal growth factor receptor (EGFR) mutation is a critical signal for the treatment strategies of patients with NSCLC. Higher response rates and prolonged progression-free survival could be obtained in patients with NSCLC harboring EGFR mutations treated with tyrosine kinase inhibitors (TKIs) when compared with traditional cytotoxic chemotherapy. However, patients with EGFR mutation treated with TKIs inevitably develop drug resistance, so predicting the duration of resistance is of great importance for selecting individual treatment strategies. Several semiquantitative metabolic parameters, e.g., maximum standard uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG), measured by PET/CT to reflect 18F-FDG metabolic activity, have been demonstrated to be powerful in predicting the status of EGFR mutation, monitoring treatment response of TKIs, and assessing the outcome of patients with NSCLC. In this review, we summarize the biological and clinical correlations between EGFR mutation status and 18F-FDG metabolic activity in NSCLC. The metabolic activity of 18F-FDG, as an extrinsic manifestation of NSCLC, could reflect the mutation status of intrinsic factor EGFR. Both of them play a critical role in guiding the implementation of treatment modalities and evaluating therapy efficacy and outcome for patients with NSCLC.

Predictive value of multiple metabolic and heterogeneity parameters of 18F-FDG PET/CT for EGFR mutations in non-small cell lung cancer

OBJECTIVES

To explore the value of multiple metabolic and heterogeneity parameters of 2-deoxy-2-[fluorine-18] fluoro-D-glucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) in predicting epidermal growth factor receptor gene (EGFR) mutations in non-small cell lung cancer (NSCLC).

MATERIALS AND METHODS

A retrospective analysis was performed by reviewing 98 patients with NSCLC who underwent EGFR mutation testing and ¹⁸F-FDG PET/CT examination in our hospital between March 2016 and March 2021. Patients were divided into an EGFR-mutant group and a wild-type group. A multivariate logistic regression analysis was performed to screen and construct a prediction model. The diagnostic performance of the model was evaluated using a receiver-operating characteristic (ROC) curve.

RESULTS

The study found that EGFR mutations were more likely to occur in women, non-smokers, and patients with peripheral lesions, shorter maximum tumor diameter, adenocarcinoma, and T1 stage cancer. Low maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), metabolic tumor volume, total lesion glycolysis, and high coefficient of variation (COV) were significantly correlated with EGFR mutations, and the area under the ROC curve (AUC) was 0.622, 0.638, 0.679, 0.687, and 0.672, respectively. Multivariate logistic regression analysis indicated that non-smokers (odds ratio (OR) = 0.109, P = 0.014), peripheral lesions (OR = 6.917, P = 0.022), low SUVmax (≤ 7.85, OR = 5.471, P = 0.001), SUVmean (≤ 5.34, OR = 0.044, P = 0.000), and high COV (≥ 106.08, OR = 0.996, P = 0.045) were independent predictors of EGFR mutations. The AUC of the prediction model established by combining the above factors was 0.926; the diagnostic efficiency was significantly higher than that of a single parameter.

CONCLUSION

Among the metabolic and heterogeneity parameters of ¹⁸F-FDG PET/CT, low SUVmax, SUVmean, and high COV were significantly associated with EGFR mutations, and the predictive value of EGFR mutations could be enhanced when combined with clinicopathological features.

Deep Radiotranscriptomics of Non-Small Cell Lung Carcinoma for Assessing Molecular and Histology Subtypes with a Data-Driven Analysis

Radiogenomic and radiotranscriptomic studies have the potential to pave the way for a holistic decision support system built on genomics, transcriptomics, radiomics, deep features and clinical parameters to assess treatment evaluation and care planning. The integration of invasive and routine imaging data into a common feature space has the potential to yield robust models for inferring the drivers of underlying biological mechanisms. In this non-small cell lung carcinoma study, a multi-omics representation comprised deep features and transcriptomics was evaluated to further explore the synergetic and complementary properties of these diverse multi-view data sources by utilizing data-driven machine learning models. The proposed deep radiotranscriptomic analysis is a feature-based fusion that significantly enhances sensitivity by up to 0.174 and AUC by up to 0.22, compared to the baseline single source models, across all experiments on the unseen testing set. Additionally, a radiomics-based fusion was also explored as an alternative methodology yielding radiomic signatures that are comparable to several previous publications in the field of radiogenomics. Furthermore, the machine learning multi-omics analysis based on deep features and transcriptomics achieved an AUC performance of up to 0.831 ± 0.09/0.925 ± 0.04 for the examined molecular and histology subtypes analysis, respectively. The clinical impact of such high-performing models can add prognostic value and lead to optimal treatment assessment by targeting specific oncogenes, namely the response of tyrosine kinase inhibitors of EGFR mutated or predicting the chemotherapy resistance of KRAS mutated tumors.

Progress and future prospective of FDG-PET/CT imaging combined with optimized procedures in lung cancer: toward precision medicine

With a 5-year overall survival of approximately 20%, lung cancer has always been the number one cancer-specific killer all over the world. As a fusion of positron emission computed tomography (PET) and computed tomography (CT), PET/CT has revolutionized cancer imaging over the past 20 years. In this review, we focused on the optimization of the function of ¹⁸F-flurodeoxyglucose (FDG)-PET/CT in diagnosis, prognostic prediction and therapy management of lung cancers by computer programs. FDG-PET/CT has demonstrated a surprising role in development of therapeutic biomarkers, prediction of therapeutic responses and long-term survival, which could be conducive to solving existing dilemmas. Meanwhile, novel tracers and optimized procedures are also developed to control the quality and improve the effect of PET/CT. With the continuous development of some new imaging agents and their clinical applications, application value of PET/CT has broad prospects in this area.